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多参数筛选优化免疫沉淀。

Multiparameter screen optimizes immunoprecipitation.

机构信息

European Research Institute for the Biology of Ageing, University Medical Centre Groningen, Groningen, 9713AV, The Netherlands.

Laboratory of Cellular & Structural Biology, The Rockefeller University, New York, NY 10065, USA.

出版信息

Biotechniques. 2024 Apr;76(4):145-152. doi: 10.2144/btn-2023-0051. Epub 2024 Feb 29.

Abstract

Immunoprecipitation (IP) coupled with mass spectrometry effectively maps protein-protein interactions when genome-wide, affinity-tagged cell collections are used. Such studies have recorded significant portions of the compositions of physiological protein complexes, providing draft 'interactomes'; yet many constituents of protein complexes still remain uncharted. This gap exists partly because high-throughput approaches cannot optimize each IP. A key challenge for IP optimization is stabilizing interactions during the transfer from cells to test tubes; failure to do so leads to the loss of genuine interactions during the IP and subsequent failure to detect. Our high-content screening method explores the relationship between chemical conditions and IP outcomes, enabling rapid empirical optimization of conditions for capturing target macromolecular assemblies.

摘要

免疫沉淀(IP)与质谱联用,在使用全基因组亲和标签细胞库时,可有效地绘制蛋白质-蛋白质相互作用图谱。此类研究记录了生理蛋白质复合物组成的重要部分,提供了草案“相互作用组”;然而,蛋白质复合物的许多成分仍然未知。这种差距的部分原因是高通量方法不能优化每个 IP。IP 优化的一个关键挑战是在从细胞转移到试管的过程中稳定相互作用;如果不能做到这一点,就会导致 IP 过程中真正的相互作用丢失,随后无法检测到。我们的高内涵筛选方法探索了化学条件与 IP 结果之间的关系,能够快速经验性地优化捕获靶大分子组装的条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b70c/11091867/4c38f4de6e3c/nihms-1986457-f0001.jpg

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